Data and Analytics Guide: BI Dashboards, KPIs and Data Warehousing

Data & Analytics

Moving from Excel reports to BI, setting KPIs, management dashboards, data warehousing, data quality and GIS guides in one place. Start deciding with data.

Most businesses are not short of data; it is just scattered. Sales live in the CRM, stock in the ERP, production on machine screens and the budget in a spreadsheet, and before every management meeting someone stitches the pieces together by hand. In Türkiye the gap is still wide: according to TurkStat's ICT Usage Survey in Enterprises 2025, only 6.5% of enterprises with 10 or more employees used business intelligence (BI) software. Quality matters as much as quantity. Writing in MIT Sloan Management Review, data quality expert Thomas C. Redman estimates the cost of bad data at 15% to 25% of revenue for most companies.

This hub collects our data and analytics articles into a reading path from first steps to decision. Start with moving from Excel reports to BI, which explains when spreadsheets stop coping and how to plan the switch. Then read how to set KPIs to decide what to measure, and our guide to choosing KPIs for a BI dashboard to see how to present them to management.

Going deeper, the infrastructure comes into focus. Our data warehouse and ETL guide explains how to bring data from several sources into one place, and data quality and duplicate records covers how to make reports trustworthy. For organisations working with spatial data, GIS for municipalities explains how to set up a city information system, while drone NDVI analysis shows how crop health maps are produced and read in agricultural and land projects.

For delivery, see our data and analytics services and our pages on BI dashboards, data warehousing and data governance and quality. Before you begin, write down the three figures that cause the most debate in management meetings, and note which system produces each one, who prepares it and how often. That short list will largely decide where you should start reading and what the first dashboard needs to cover.

Other guides that put data to work

Production and operations: how to calculate OEE, AI demand forecasting and anomaly detection.
Sales and data sources: CRM and the sales pipeline, API integration and when to replace spreadsheets with software.
Field data: soil moisture sensors for precision agriculture.

Start here

BI Dashboard KPIs: Choosing, Defining and Automating the Metrics Your Management Team Uses

The right BI dashboard KPIs decide whether management uses the dashboard at all. How to choose, define and feed each metric from trusted data.

Read the guide

All guides in this topic (2)

Data & Analytics

Data Warehouse and ETL Explained: Architecture Options and When Your Business Needs One

What a data warehouse is, how ETL works and when you need one. A plain-English guide to bringing ERP, CRM and production data into one trusted source.

24 September 2026 · 8 min read

Questions we hear most often

Frequently Asked Questions

How is a BI dashboard different from an Excel report?

An Excel report is usually a hand-built snapshot of one moment. A BI dashboard connects to the source systems, refreshes itself on a set schedule and lets users drill down from a headline figure into the detail. Less time goes on preparing reports, and everyone discusses the same number rather than competing versions.

Does every business need a data warehouse?

No. A small business reporting from a single ERP may be well served by a dashboard connected directly to it. A data warehouse becomes necessary when data comes from several systems, when you need comparisons across previous years, or when reporting slows down the source systems. Base the decision on your data sources and reporting needs.

What makes a good KPI?

A good KPI is tied directly to a goal, is defined the same way for everyone, draws its data automatically from a reliable source and has a named owner. It must also be able to change a decision: if nobody knows what to do when the figure worsens, it is information rather than a KPI. A few well-chosen indicators beat a long list.

Where do data quality problems start?

Most start at the point of entry: the same customer created twice with different spellings, mandatory fields left blank, different codes used in different systems. A lasting fix combines a one-off clean-up with entry rules, a single master data source and regular quality checks, so that the problems do not simply creep back in.

Have a different question? Ask Us